import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from typing import Any


class GoogLeNet(nn.Module):
    def __init__(self, num_classes=1000, aux_logits=True):
        super(GoogLeNet, self).__init__()

        self.aux_logits = aux_logits

        self.conv1 = BasicConv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.conv2 = BasicConv2d(32, 32, kernel_size=1)
        self.conv3 = BasicConv2d(32, 96, kernel_size=3, padding=1)
        self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception3a = Inception(96, 32, 48, 64, 8, 16, 16)
        self.inception3b = Inception(128, 64, 64, 96, 16, 48, 32)
        self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception4a = Inception(240, 96, 48, 104, 8, 24, 32)
        self.inception4b = Inception(256, 80, 56, 112, 12, 32, 32)
        self.inception4c = Inception(256, 64, 64, 128, 12, 32, 32)
        self.inception4d = Inception(256, 56, 72, 144, 16, 32, 32)
        self.inception4e = Inception(264, 128, 80, 160, 16, 64, 64)
        self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception5a = Inception(416, 128, 80, 160, 16, 64, 64)
        self.inception5b = Inception(416, 192, 96, 192, 24, 64, 64)

        if self.aux_logits:
            self.aux1 = InceptionAux(256, num_classes)
            self.aux2 = InceptionAux(264, num_classes)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(0.2)
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x: Tensor):
        # N x 1 x 56 x 56
        x = self.conv1(x)
        # N x 32 x 56 x 56
        x = self.maxpool1(x)
        # N x 32 x 28 x 28
        x = self.conv2(x)
        # N x 32 x 28 x 28
        x = self.conv3(x)
        # N x 96 x 28 x 28
        x = self.maxpool2(x)

        # N x 96 x 14 x 14
        x = self.inception3a(x)
        # N x 128 x 14 x 14
        x = self.inception3b(x)
        # N x 240 x 14 x 14
        x = self.maxpool3(x)
        # N x 240 x 7 x 7
        x = self.inception4a(x)
        # N x 256 x 7 x 7
        if self.aux1 is not None:
            if self.training:
                aux1 = self.aux1(x)

        x = self.inception4b(x)
        # N x 256 x 7 x 7
        x = self.inception4c(x)
        # N x 256 x 7 x 7
        x = self.inception4d(x)
        # N x 264 x 7 x 7
        if self.aux2 is not None:
            if self.training:
                aux2 = self.aux2(x)

        x = self.inception4e(x)
        # N x 416 x 7 x 7
        x = self.maxpool4(x)
        # N x 416 x 3 x 3
        x = self.inception5a(x)
        # N x 416 x 3 x 3
        x = self.inception5b(x)
        # N x 512 x 3 x 3

        x = self.avgpool(x)
        # N x 512 x 1 x 1
        x = torch.flatten(x, 1)
        # N x 512
        x = self.dropout(x)
        x = self.fc(x)
        # N x 10 (num_classes)
        return x, aux2, aux1


class Inception(nn.Module):

    def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
        super(Inception, self).__init__()
        self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)

        self.branch2 = nn.Sequential(
            BasicConv2d(in_channels, ch3x3red, kernel_size=1),
            BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)
        )

        self.branch3 = nn.Sequential(
            BasicConv2d(in_channels, ch5x5red, kernel_size=1),
            # Here, kernel_size=3 instead of kernel_size=5 is a known bug.
            # Please see https://github.com/pytorch/vision/issues/906 for details.
            BasicConv2d(ch5x5red, ch5x5, kernel_size=3, padding=1)
        )

        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
            BasicConv2d(in_channels, pool_proj, kernel_size=1)
        )

    def forward(self, x: Tensor) -> Tensor:
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)

        outputs = [branch1, branch2, branch3, branch4]
        return torch.cat(outputs, 1)


class InceptionAux(nn.Module):

    def __init__(self, in_channels, num_classes):
        super(InceptionAux, self).__init__()
        self.conv = BasicConv2d(in_channels, 128, kernel_size=1)

        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, num_classes)

    def forward(self, x: Tensor) -> Tensor:
        # aux1: N x 256 x 14 x 14, aux2: N x 264 x 14 x 14
        x = F.adaptive_avg_pool2d(x, (4, 4))
        # aux1: N x 256 x 4 x 4, aux2: N x 264 x 4 x 4
        x = self.conv(x)
        # N x 128 x 4 x 4
        x = torch.flatten(x, 1)
        # N x 2048
        x = F.relu(self.fc1(x), inplace=True)
        # N x 1024
        x = F.dropout(x, 0.7, training=self.training)
        # N x 1024
        x = self.fc2(x)
        # N x 10 (num_classes)

        return x


class BasicConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, **kwargs: Any):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)

    def forward(self, x: Tensor) -> Tensor:
        x = self.conv(x)
        x = self.bn(x)
        return F.relu(x, inplace=True)